Intro to Machine Learning

Welcome to Machine Learning

Meet with Sebastian and Katie to discuss machine learning.

Naive Bayes

Learn about classification, training and testing, and run a naive Bayes classifier using Scikit Learn.

SVM

Build an intuition about how support vector machines (SVMs) work and implement one using scikit-learn.

Decision Trees

Learn about how the decision tree algorithm works, including the concepts of entropy and information gain.

Choose Your Own Algorithm

In this mini project, you will extend your toolbox of algorithms by choosing your own algorithm to classify terrain data, including k-nearest neighbors, AdaBoost, and random forests.

Datasets and Questions

Find out about the Enron data set used in the next lessons and mini-projects.

Regressions

See how we can model continuous data using linear regression.

Outliers

Sebastian discusses outlier detection and removal.

Clustering

Learn about what unsupervised learning is and find out how to use scikit-learn's k-means algorithm.

Feature Scaling

Learn about feature rescaling and find out which algorithms require feature rescaling before use.

Feature Selection

Katie discusses when and why to use feature selection, and provides some methods for doing this.

Text Learning

Find out how to use text data in your machine learning algorithm.

PCA

Learn about data dimensionality and reducing the number of dimensions with principal component analysis (PCA).

Validation

Learn more about testing, training, cross validation, and parameter grid searches in this lesson.

Evaluation Metrics

How do we know if our classifier is performing well? Katie discusses different evaluation metrics for classifiers in this lesson.

Tying It All Together

Spend some time reflecting on the course material with Sebastian and Katie!

Final Project